Climate change induced by global warming will produce more frequent heatwaves and intensify urban heat islands, thereby increasing heating related risks. Given this scenario, it is important to mitigate heat on urban ecosystems to promote their resilience. This study aims to develop a method to identify heat vulnerable urban areas by considering the land surface temperature (LST) distribution and landscape patterns. Methods included the creation of a cellular space to represent a given urban environment and integrate multidisciplinary databases; the use of Landsat-8 satellite images to estimate fraction vegetation cover, normalized difference moisture indices emissivity, and albedo; and exploratory spatial analyses using Moran's global and local indices to identify landscape patterns associated with Local Climate Zones (LCZ). Analyses revealed that the estimated variables are suitable for explaining LST, which is autocorrelated in space, despite seasonal variations. The methods made it possible to identify heat vulnerable areas, which should be considered when developing adaptation policies, and that heating is associated with areas composed of both compact low rise (LCZ3) and large low rise buildings (LCZ8), whereas cooling is associated with dense trees (LCZA), and when vegetation is associated with both open high rise (LCZ4) and open low rise buildings (LCZ6).